skip to main content

Introduction

As businesses move to data science to exploit the potential of their data, from creating business intelligence analytics to building artificial intelligence (AI)/machine learning (ML) applications, they are beginning to experience the complexities of bringing such complicated systems to production.

Highlights

  • DataKitchen brings lifecycle discipline to data science operations, helping deliver business insights by enabling the development and deployment of innovative and iterative data analytic pipelines.

Features and Benefits

  • Learn what DataOps means, how it is related to Agile, DevOps, and data science.
  • Assess DataKitchen's end-to-end DataOps solution. It requires minimal programming and integrates with core data engineering, science, governance and visualization tools currently used by the business.

Key questions answered

  • What is DataOps and how does it relate to current data science practices?
  • How does DataKitchen tackle the problem of complexity in managing multiple machine learning application lifecycles?

Table of contents

Summary

  • Catalyst
  • Key messages
  • Ovum view

Recommendations for enterprises

  • Why put DataKitchen on your radar?

Highlights

  • The DataKitchen platform
  • Background
  • Current position
  • On the roadmap

Data sheet

  • Key facts

Appendix

  • On the Radar
  • Authors